Project: GRAPPLE - Iterative modelling of gene regulatory interactions underlying stress, disease and ageing in C. elegans

Understanding the regulatory control of some biomedically important phenotypes at the level of genes is problematic. We propose to use the expression QTL (eQTL) technique to define gene regulatory interactions on the genomic scale. These functionally defined gene-gene interactions will then be integrated into a network-based model which will be complemented by data from other, less informative techniques. Predictions derived form this model will then be used to define an iterative series of validation tests, the results of which will feed back into the modification and improvement of the model. In addition, we shall develop novel computational approaches to the construction of networks and the identification of sub-networks contributing to different phenotypes.

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The project is based on the availability of a validated set of 200 recombinant inbred lines (RILs) of the nematode C. elegans and the use of the eQTL technique. A technical breakthrough in DNA sequencing offers the ultimate specification of SNPs via determination of whole genome sequences for all of the 200 RILs, thereby giving an unsurpassed definition of genes within genetic loci. The rapidly developing field of graph theory and its application to biological molecular networks offers a much stronger methodology with which to generate informative networks of gene regulatory interactions, and we shall develop techniques adapted to this purpose. Finally, we focus on thermally adaptive phenotypes of C. elegans that are very clearcut and reproducible, and which show great variations across the set of RILs. The thermal stress response and adaptation is highly correlated with resistance to numerous other environmental stresses, pathogen resistance and lifespan extension. Our network will provide a foundation for understanding these biomedically relevant traits.


  1. To construct predictive models that describe how gene regulatory networks control complex biomedically relevant phenotypes in a simple animal.
  2. To understand how natural genetic variation impacts on these networks to affect phenotypic variation in stress tolerance, disease-resistance and lifespan.
  3. To identify and validate key novel regulators and pathways of stress resistance, disease resistance and longevity and to evaluate the crosstalk between these systems.
  4. To develop and disseminate new combined experimental-computational approaches for the iterative construction / refinement of regulatory network models underlying complex genetic traits in animals.
  5. To develop and evaluate new methods for comparative analysis of network structure in relation to longevity and stress responses.